Artificial Intelligence in Manufacturing

Problem: NC programmers spend hours defining and validating machining strategies, feeds, speeds, and toolpaths. Less experienced programmers often rely on conservative parameters or manually recreate proven processes, increasing programming time and leaving machining efficiency untapped.

What DELMIA does: DELMIA Machining uses AI-assisted programming capabilities, validated through virtual twin simulation, to recommend machining operations, toolpaths, and cutting parameters based on proven machining know-how and similar part geometries.

Human role: NC Programmers review, refine, and approve suggested operations based on machine behavior, tooling, material requirements, shop-floor constraints, and production priorities. AI assists by reducing manual programming effort and surfacing optimized options faster.

Measured outcome: 40–75% reduction in NC programming time through feature recognition, programming reuse, and automation workflows, alongside 30–70% machining cycle time improvement in applicable scenarios

Problem: Layer-by-layer defects in additive manufacturing are invisible until post-process inspection, at which point the part is already scrapped and cycle time is lost.

What DELMIA does: Layer and melt images are used to reconstruct an as-built 3D model. Computer vision classifies defect types in real time, triggering process corrections before the next layer deposits.

Human role: Quality engineers review classifications, validate flagged anomalies, and adjust process parameters. The AI catches what the human cannot observe in real time.

Measured outcome: Earlier defect detection reduces scrap rates. Specific figures depend on material and machine configuration, but pilot deployments in aerospace show >30% reduction in post-process reject rates.

Problem: Complex assembly and quality inspection tasks depend on individual operator expertise that is difficult to transfer, slow to train, and vulnerable to workforce turnover.

What DELMIA does: Generative AI, combined with augmented reality overlays, guides operators through complex tasks with real-time visual instruction. AR-enabled quality checks replace manual visual inspection with machine-assisted precision.

Human role: Operators perform the physical work, confirm AI-guided steps, and escalate anomalies. The AR layer extends capability rather than replacing it.

Measured outcome: Reduction in cost of poor quality; shorter onboarding time for new operators. One aerospace customer reported 40% reduction in first-time quality failures on complex assemblies.

Problem: Factory layouts change. Keeping the virtual twin synchronized with physical reality requires manual measurement and CAD updates, a task that falls behind and creates a growing gap between the model and the floor.

What DELMIA does: AI detects robots and equipment within point cloud scans, retrieves the corresponding CAD models, and positions them in the factory digital model automatically.

Human role: Engineers validate placements, correct misidentifications, and approve updates. Keeps the virtual twin current with significantly less manual effort.

Measured outcome: Factory survey-to-updated-model time reduced from weeks to hours in tested deployments. Quality of the virtual twin improves, which increases the accuracy of downstream AI recommendations.

Problem: Manufacturing BOM generation and process planning for new products draws heavily on past industrializations work that is currently done manually, inconsistently, and slowly.

What DELMIA does: AI generates MBOM proposals, process sequences, work instructions, and resource programming by drawing on historical industrialization data. Automated consistency checks flag EBOM/MBOM discrepancies before they reach the floor.

Human role: Process engineers review AI-generated plans, apply product-specific engineering judgment, and approve. The AI provides a structured first draft; the engineer ensures it's fit for purpose.

Measured outcome: Estimated 50%+ reduction in manufacturing preparation lead time. Right-first-time rates improve because data consistency checks catch structural errors early.

Problem: Uncontrolled schedule variation. Manufacturing schedules change constantly, but teams often lack clarity on which deviations matter, why they occur, and how they impact performance, limiting their ability to learn and improve future decisions.

What DELMIA does: DELMIA Scheduling Intelligence turns schedule history into actionable insight by comparing planned and actual performance, highlighting meaningful deviations, and using AI-based suggestions to help planners understand causes, impacts, and improvement opportunities.

Human role: Schedulers evaluate proposed resolutions, weigh trade-offs (customer priority, cost, asset utilization), and make the call. The system surfaces the options; the planner owns the decision. 

Measured outcome: +20% estimated improvement in on-time delivery performance; -50% reduction in scheduling time from improved cross-team communication efficiency.